Free Machine Learning ebooks. Categorized directory of free Machine Learning books. Read online or download free ebooks in different formats.
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A First Encounter with Machine Learning by Max Welling
 
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Ebook Details

Author
Max Welling
Publisher
University of California Irvine 2011
Description: The book you see before you is meant for those starting out in the field of machine learning.
Bayesian Reasoning and Machine Learning by David Barber
 
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Author
David Barber
Publisher
Cambridge University Press 2011
The book is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.
Introduction to Machine Learning by Amnon Shashua
 
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Author
Amnon Shashua
Publisher
arXiv 2009
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer
 
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Author
C. Weber, M. Elshaw, N. M. Mayer
Publisher
InTech 2008
The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields.
Statistical Foundations of Machine Learning by Gianluca Bontempi, Souhaib Ben Taieb
 
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Author
Gianluca Bontempi, Souhaib Ben Taieb
Publisher
2017
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. In particular, we focus on supervised learning problems.
Statistical Learning and Sequential Prediction by Alexander Rakhlin, Karthik Sridharan
 
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Author
Alexander Rakhlin, Karthik Sridharan
Publisher
University of Pennsylvania 2014
This course will focus on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. 
Machine Learning by Abdelhamid Mellouk, Abdennacer Chebira
 
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Author
Abdelhamid Mellouk, Abdennacer Chebira
Publisher
InTech 2009
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, 
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David
 
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Author
Shai Shalev-Shwartz, Shai Ben-David
Publisher
Cambridge University Press 2014
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
Deep Learning Tutorial by LISA lab
 
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Author
LISA lab
Publisher
University of Montreal 2015
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU....
Lecture Notes in Machine Learning by Zdravko Markov
 
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Ebook Details

Author
Zdravko Markov
Publisher
Central Connecticut State University 2003
Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning; Explanation-based Learning.
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